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How to design, analyse and report cluster randomised trials in medicine and health related research / / Michael J. Campbell and Stephen J. Walters
| How to design, analyse and report cluster randomised trials in medicine and health related research / / Michael J. Campbell and Stephen J. Walters |
| Autore | Campbell Michael J. <1950-> |
| Pubbl/distr/stampa | Chichester, England : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (268 p.) |
| Disciplina | 610.72/4 |
| Collana | Statistics in Practice |
| Soggetto topico |
Randomized Controlled Trials as Topic
Data Interpretation, Statistical Health Services Research - method Research Design |
| ISBN |
1-118-76360-2
1-118-76345-9 1-118-76359-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Acronyms and abbreviations; Chapter 1 Introduction; 1.1 Randomised controlled trials; 1.1.1 A-Allocation at random; 1.1.2 B-Blindness; 1.1.3 C-Control; 1.2 Complex interventions; 1.3 History of cluster randomised trials; 1.4 Cohort and field trials; 1.5 The field/community trial; 1.5.1 The REACT trial; 1.5.2 The Informed Choice leaflets trial; 1.5.3 The Mwanza trial; 1.5.4 The paramedics practitioner trial; 1.6 The cohort trial; 1.6.1 The PoNDER trial; 1.6.2 The DESMOND trial; 1.6.3 The Diabetes Care from Diagnosis trial; 1.6.4 The REPOSE trial
1.6.5 Other examples of cohort cluster trials 1.7 Field versus cohort designs; 1.8 Reasons for cluster trials; 1.9 Between- and within-cluster variation; 1.10 Random-effects models for continuous outcomes; 1.10.1 The model; 1.10.2 The intracluster correlation coefficient; 1.10.3 Estimating the intracluster correlation (ICC) coefficient; 1.10.4 Link between the Pearson correlation coefficient and the intraclass correlation coefficient; 1.11 Random-effects models for binary outcomes; 1.11.1 The model; 1.11.2 The ICC for binary data; 1.11.3 The coefficient of variation 1.11.4 Relationship between cvc and ρ for binary data 1.12 The design effect; 1.13 Commonly asked questions; 1.14 Websources; Exercise; Appendix 1.A; Chapter 2 Design issues; 2.1 Introduction; 2.2 Issues for a simple intervention; 2.2.1 Phases of a trial; 2.2.1.1 Preclinical; 2.2.1.2 Sequence of phases; 2.2.2 'Pragmatic' and 'explanatory' trials; 2.2.3 Intention-to-treat and per-protocol analyses; 2.2.4 Non-inferiority and equivalence trials; 2.3 Complex interventions; 2.3.1 Design of complex interventions; 2.3.1.1 Theory (preclinical); 2.3.2 Phase I modelling/qualitative designs 2.3.3 Pilot or feasibility studies 2.3.4 Example of pilot/feasibility studies in cluster trials; 2.4 Recruitment bias; 2.5 Matched-pair trials; 2.5.1 Design of matched-pair studies; 2.5.2 Limitations of matched-pairs designs; 2.5.3 Example of matched-pair design: The Family Heart Study; 2.6 Other types of designs; 2.6.1 Cluster factorial designs; 2.6.2 Example cluster factorial trial; 2.6.3 Cluster crossover trials; 2.6.4 Example of a cluster crossover trial; 2.6.5 Stepped wedge; 2.6.6 Pseudorandomised trials; 2.7 Other design issues; 2.8 Strategies for improving precision; 2.9 Randomisation 2.9.1 Reasons for randomisation 2.9.2 Simple randomisation; 2.9.3 Stratified randomisation; 2.9.4 Restricted randomisation; 2.9.5 Minimisation; Exercise; Appendix 2.A; Chapter 3 Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial?; 3.1 Introduction; 3.1.1 Justification of the requirement for a sample size; 3.1.2 Significance tests, P-values and power; 3.1.3 Sample size and cluster trials; 3.2 Sample size for continuous data-comparing two means; 3.2.1 Basic formulae; 3.2.2 The design effect (DE) in cluster RCTs; 3.2.3 Example from general practice 3.3 Sample size for binary data-comparing two proportions |
| Record Nr. | UNINA-9910139141103321 |
Campbell Michael J. <1950->
|
||
| Chichester, England : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
How to design, analyse and report cluster randomised trials in medicine and health related research / / Michael J. Campbell and Stephen J. Walters
| How to design, analyse and report cluster randomised trials in medicine and health related research / / Michael J. Campbell and Stephen J. Walters |
| Autore | Campbell Michael J. <1950-> |
| Pubbl/distr/stampa | Chichester, England : , : Wiley, , 2014 |
| Descrizione fisica | 1 online resource (268 p.) |
| Disciplina | 610.72/4 |
| Collana | Statistics in Practice |
| Soggetto topico |
Randomized Controlled Trials as Topic
Data Interpretation, Statistical Health Services Research - method Research Design |
| ISBN |
1-118-76360-2
1-118-76345-9 1-118-76359-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright; Contents; Preface; Acronyms and abbreviations; Chapter 1 Introduction; 1.1 Randomised controlled trials; 1.1.1 A-Allocation at random; 1.1.2 B-Blindness; 1.1.3 C-Control; 1.2 Complex interventions; 1.3 History of cluster randomised trials; 1.4 Cohort and field trials; 1.5 The field/community trial; 1.5.1 The REACT trial; 1.5.2 The Informed Choice leaflets trial; 1.5.3 The Mwanza trial; 1.5.4 The paramedics practitioner trial; 1.6 The cohort trial; 1.6.1 The PoNDER trial; 1.6.2 The DESMOND trial; 1.6.3 The Diabetes Care from Diagnosis trial; 1.6.4 The REPOSE trial
1.6.5 Other examples of cohort cluster trials 1.7 Field versus cohort designs; 1.8 Reasons for cluster trials; 1.9 Between- and within-cluster variation; 1.10 Random-effects models for continuous outcomes; 1.10.1 The model; 1.10.2 The intracluster correlation coefficient; 1.10.3 Estimating the intracluster correlation (ICC) coefficient; 1.10.4 Link between the Pearson correlation coefficient and the intraclass correlation coefficient; 1.11 Random-effects models for binary outcomes; 1.11.1 The model; 1.11.2 The ICC for binary data; 1.11.3 The coefficient of variation 1.11.4 Relationship between cvc and ρ for binary data 1.12 The design effect; 1.13 Commonly asked questions; 1.14 Websources; Exercise; Appendix 1.A; Chapter 2 Design issues; 2.1 Introduction; 2.2 Issues for a simple intervention; 2.2.1 Phases of a trial; 2.2.1.1 Preclinical; 2.2.1.2 Sequence of phases; 2.2.2 'Pragmatic' and 'explanatory' trials; 2.2.3 Intention-to-treat and per-protocol analyses; 2.2.4 Non-inferiority and equivalence trials; 2.3 Complex interventions; 2.3.1 Design of complex interventions; 2.3.1.1 Theory (preclinical); 2.3.2 Phase I modelling/qualitative designs 2.3.3 Pilot or feasibility studies 2.3.4 Example of pilot/feasibility studies in cluster trials; 2.4 Recruitment bias; 2.5 Matched-pair trials; 2.5.1 Design of matched-pair studies; 2.5.2 Limitations of matched-pairs designs; 2.5.3 Example of matched-pair design: The Family Heart Study; 2.6 Other types of designs; 2.6.1 Cluster factorial designs; 2.6.2 Example cluster factorial trial; 2.6.3 Cluster crossover trials; 2.6.4 Example of a cluster crossover trial; 2.6.5 Stepped wedge; 2.6.6 Pseudorandomised trials; 2.7 Other design issues; 2.8 Strategies for improving precision; 2.9 Randomisation 2.9.1 Reasons for randomisation 2.9.2 Simple randomisation; 2.9.3 Stratified randomisation; 2.9.4 Restricted randomisation; 2.9.5 Minimisation; Exercise; Appendix 2.A; Chapter 3 Sample size: How many subjects/clusters do I need for my cluster randomised controlled trial?; 3.1 Introduction; 3.1.1 Justification of the requirement for a sample size; 3.1.2 Significance tests, P-values and power; 3.1.3 Sample size and cluster trials; 3.2 Sample size for continuous data-comparing two means; 3.2.1 Basic formulae; 3.2.2 The design effect (DE) in cluster RCTs; 3.2.3 Example from general practice 3.3 Sample size for binary data-comparing two proportions |
| Record Nr. | UNINA-9910828428003321 |
|
Campbell Michael J. <1950->
|
||
| Chichester, England : , : Wiley, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics at square two : understanding modern statistical applications in medicine / / Michael J. Campbell and Richard M. Jacques
| Statistics at square two : understanding modern statistical applications in medicine / / Michael J. Campbell and Richard M. Jacques |
| Autore | Campbell Michael J. <1950-> |
| Edizione | [Third edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons Ltd, , [2023] |
| Descrizione fisica | 1 online resource (248 pages) |
| Disciplina | 610.72 |
| Soggetto topico |
Medical statistics
Medical statistics - Computer programs |
| ISBN |
1-119-40140-2
1-119-40139-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Title page -- Copyright -- Table of Contents -- Preface -- 1 Models, Tests and Data -- 1.1 Types of Data -- 1.2 Confounding, Mediation and Effect Modification -- 1.3 Causal Inference -- 1.4 Statistical Models -- 1.5 Results of Fitting Models -- 1.6 Significance Tests -- 1.7 Confidence Intervals -- 1.8 Statistical Tests Using Models -- 1.9 Many Variables -- 1.10 Model Fitting and Analysis: Exploratory and Confirmatory Analyses -- 1.11 Computer-intensive Methods -- 1.12 Missing Values -- 1.13 Bayesian Methods -- 1.14 Causal Modelling -- 1.15 Reporting Statistical Results in the Medical Literature -- 1.16 Reading Statistics in the Medical Literature -- 2 Multiple Linear Regression -- 2.1 The Model -- 2.2 Uses of Multiple Regression -- 2.3 Two Independent Variables -- 2.3.1 One Continuous and One Binary Independent Variable -- 2.3.2 Two Continuous Independent Variables -- 2.3.3 Categorical Independent Variables -- 2.4 Interpreting a Computer Output -- 2.4.1 One Continuous Variable -- 2.4.2 One Continuous Variable and One Binary Independent Variable -- 2.4.3 One Continuous Variable and One Binary Independent Variable with Their Interaction -- 2.4.4 Two Independent Variables: Both Continuous -- 2.4.5 Categorical Independent Variables -- 2.5 Examples in the Medical Literature -- 2.5.1 Analysis of Covariance: One Binary and One Continuous Independent Variable -- 2.5.2 Two Continuous Independent Variables -- 2.6 Assumptions Underlying the Models -- 2.7 Model Sensitivity -- 2.7.1 Residuals, Leverage and Influence -- 2.7.2 Computer Analysis: Model Checking and Sensitivity -- 2.8 Stepwise Regression -- 2.9 Reporting the Results of a Multiple Regression -- 2.10 Reading about the Results of a Multiple Regression -- 2.11 Frequently Asked Questions -- 2.12 Exercises: Reading the Literature -- 3 Multiple Logistic Regression -- 3.1 Quick Revision.
3.2 The Model -- 3.2.1 Categorical Covariates -- 3.3 Model Checking -- 3.3.1 Lack of Fit -- 3.3.2 "Extra-binomial" Variation or "Over Dispersion" -- 3.3.3 The Logistic Transform is Inappropriate -- 3.4 Uses of Logistic Regression -- 3.5 Interpreting a Computer Output -- 3.5.1 One Binary Independent Variable -- 3.5.2 Two Binary Independent Variables -- 3.5.3 Two Continuous Independent Variables -- 3.6 Examples in the Medical Literature -- 3.6.1 Comment -- 3.7 Case-control Studies -- 3.8 Interpreting Computer Output: Unmatched Case-control Study -- 3.9 Matched Case-control Studies -- 3.10 Interpreting Computer Output: Matched Case-control Study -- 3.11 Example of Conditional Logistic Regression in the Medical Literature -- 3.11.1 Comment -- 3.12 Alternatives to Logistic Regression -- 3.13 Reporting the Results of Logistic Regression -- 3.14 Reading about the Results of Logistic Regression -- 3.15 Frequently Asked Questions -- 3.16 Exercise -- 4 Survival Analysis -- 4.1 Introduction -- 4.2 The Model -- 4.3 Uses of Cox Regression -- 4.4 Interpreting a Computer Output -- 4.5 Interpretation of the Model -- 4.6 Generalisations of the Model -- 4.6.1 Stratified Models -- 4.6.2 Time Dependent Covariates -- 4.6.3 Parametric Survival Models -- 4.6.4 Competing Risks -- 4.7 Model Checking -- 4.8 Reporting the Results of a Survival Analysis -- 4.9 Reading about the Results of a Survival Analysis -- 4.10 Example in the Medical Literature -- 4.10.1 Comment -- 4.11 Frequently Asked Questions -- 4.12 Exercises -- 5 Random Effects Models -- 5.1 Introduction -- 5.2 Models for Random Effects -- 5.3 Random vs Fixed Effects -- 5.4 Use of Random Effects Models -- 5.4.1 Cluster Randomised Trials -- 5.4.2 Repeated Measures -- 5.4.3 Sample Surveys -- 5.4.4 Multi-centre Trials -- 5.5 Ordinary Least Squares at the Group Level -- 5.6 Interpreting a Computer Output. 5.6.1 Different Methods of Analysis -- 5.6.2 Likelihood and gee -- 5.6.3 Interpreting Computer Output -- 5.7 Model Checking -- 5.8 Reporting the Results of Random Effects Analysis -- 5.9 Reading about the Results of Random Effects Analysis -- 5.10 Examples of Random Effects Models in the Medical Literature -- 5.10.1 Cluster Trials -- 5.10.2 Repeated Measures -- 5.10.3 Comment -- 5.10.4 Clustering in a Cohort Study -- 5.10.5 Comment -- 5.11 Frequently Asked Questions -- 5.12 Exercises -- 6 Poisson and Ordinal Regression -- 6.1 Poisson Regression -- 6.2 The Poisson Model -- 6.3 Interpreting a Computer Output: Poisson Regression -- 6.4 Model Checking for Poisson Regression -- 6.5 Extensions to Poisson Regression -- 6.6 Poisson Regression Used to Estimate Relative Risks from a 2 × 2 Table -- 6.7 Poisson Regression in the Medical Literature -- 6.8 Ordinal Regression -- 6.9 Interpreting a Computer Output: Ordinal Regression -- 6.10 Model Checking for Ordinal Regression -- 6.11 Ordinal Regression in the Medical Literature -- 6.12 Reporting the Results of Poisson or Ordinal Regression -- 6.13 Reading about the Results of Poisson or Ordinal Regression -- 6.14 Frequently Asked Question -- 6.15 Exercises -- 7 Meta-analysis -- 7.1 Introduction -- 7.2 Models for Meta-analysis -- 7.3 Missing Values -- 7.4 Displaying the Results of a Meta-analysis -- 7.5 Interpreting a Computer Output -- 7.6 Examples from the Medical Literature -- 7.6.1 Example of a Meta-analysis of Clinical Trials -- 7.6.2 Example of a Meta-analysis of Case-control Studies -- 7.7 Reporting the Results of a Meta-analysis -- 7.8 Reading about the Results of a Meta-analysis -- 7.9 Frequently Asked Questions -- 7.10 Exercise -- 8 Time Series Regression -- 8.1 Introduction -- 8.2 The Model -- 8.3 Estimation Using Correlated Residuals -- 8.4 Interpreting a Computer Output: Time Series Regression. 8.5 Example of Time Series Regression in the Medical Literature -- 8.6 Reporting the Results of Time Series Regression -- 8.7 Reading about the Results of Time Series Regression -- 8.8 Frequently Asked Questions -- 8.9 Exercise -- Appendix 1 Exponentials and Logarithms -- Appendix 2 Maximum Likelihood and Significance Tests -- A2.1 Binomial Models and Likelihood -- A2.2 The Poisson Model -- A2.3 The Normal Model -- A2.4 Hypothesis Testing: the Likelihood Ratio Test -- A2.5 The Wald Test -- A2.6 The Score Test -- A2.7 Which Method to Choose? -- A2.8 Confidence Intervals -- A2.9 Deviance Residuals for Binary Data -- A2.10 Example: Derivation of the Deviances and Deviance Residuals Given in Table 3.3 -- A2.10.1 Grouped Data -- A2.10.2 Ungrouped Data -- Appendix 3 Bootstrapping and Variance Robust Standard Errors -- A3.1 The Bootstrap -- A3.2 Example of the Bootstrap -- A3.3 Interpreting a Computer Output: The Bootstrap -- A3.3.1 Two-sample T-test with Unequal Variances -- A3.4 The Bootstrap in the Medical Literature -- A3.5 Robust or Sandwich Estimate SEs -- A3.6 Interpreting a Computer Output: Robust SEs for Unequal Variances -- A3.7 Other Uses of Robust Regression -- A3.8 Reporting the Bootstrap and Robust SEs in the Literature -- A3.9 Frequently Asked Question -- Appendix 4 Bayesian Methods -- A4.1 Bayes' Theorem -- A4.2 Uses of Bayesian Methods -- A4.3 Computing in Bayes -- A4.4 Reading and Reporting Bayesian Methods in the Literature -- A4.5 Reading about the Results of Bayesian Methods in the Medical Literature -- Appendix 5 R codes -- A5.1 R Code for Chapter 2 -- A5.3 R Code for Chapter 3 -- A5.4 R Code for Chapter 4 -- A5.5 R Code for Chapter 5 -- A5.6 R Code for Chapter 6 -- A5.7 R Code for Chapter 7 -- A5.8 R Code for Chapter 8 -- A5.9 R Code for Appendix 1 -- A5.10 R Code for Appendix 2 -- A5.11 R Code for Appendix 3 -- Answers to Exercises. Glossary -- Index -- End User License Agreement. |
| Record Nr. | UNINA-9910830218303321 |
|
Campbell Michael J. <1950->
|
||
| Hoboken, New Jersey : , : John Wiley & Sons Ltd, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||